Image classification models may be used to classify or categorize images based on their parameters and/or extracted attributes. Using machine learning methods, such as convolutional neural networks (CNNs), it is possible to categorize images. For example, images may be manipulated to be run in a CNN to either recognize or classify the images. Further, CNNs or—similar machine learning methods—may be employed to extract image parameters that are then correlated with other parameters or attributes for classification. These methods can be powerful to identify or categorize images because the correlations can be highly accurate. Moreover, CNNs may be updated or retrained frequently to accurately capture image parameters.
However, sharing or displaying results of an image classification model can be challenging. Frequently, a classification model returns multiple potential classification results based on a sample image, with each one of the results being associated with a different confidence level. In some embodiments, only the result with highest confidence or score may be presented to the user. The highest confidence result may, nonetheless, be inaccurate (e.g., the model is not properly tuned) or may be undesirable to the user. Hence, as an alternative, multiple classification results may be presented for the user's review. However, limited screen space in portable devices and lower resolutions may result in a review process that is tedious and difficult. In particular, users of portable devices may be required to scroll through multiple different classification options to review and analyze different results. Further, reviewing multiple classification options in portable devices may clutter the limited screen space, making it difficult to identify or select an adequate result. Alternatives of displaying isolated results in independent windows may result in poor user experience because users would be required to scroll through different graphical user interfaces (GUIs) to identify a matching result. Moreover, because portable devices are normally subject to data caps and reduced bandwidths, in many situations it is impracticable to quickly send the results to a portable device.
The disclosed systems and methods for generating graphical user interfaces address one or more of the problems set forth above and/or other problems in the prior art.